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Hardware and compiler techniques for mapping data-parallel programs with divergent control flow to SIMD architectures have recently enabled the emergence of new GPGPU programming models such as CUDA and OpenCL. Although this technology is widely used, commodity GPUs use different schemes to implement it, and the performance limitations of these different schemes under real workloads are not well understood. This paper identifies important classes of program control flows, and characterizes their presence in real workloads. It is shown that most existing techniques handle structured control flow efficiently, some are incapable of executing unstructured control flow directly, and none handles unstructured control flow efficiently.
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